A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Evaluation and Interpretation of Tourist Satisfaction for Local Korean Festivals Using Explainable AI
In this paper, we propose using explainable artificial intelligence (XAI) techniques to predict and interpret the effects of local festival components on tourist satisfaction. We use data-driven analytics, including prediction, interpretation, and utilization phases, to help festivals establish a tourism strategy. Ultimately, this study aims to identify the most significant variables in local tourism strategy and to predict tourist satisfaction. To do so, we conducted an experimental study to compare the prediction accuracy of representative predictive algorithms. We then built a surrogate model based on a game theory-based framework, known as SHapley Additive exPlanations (SHAP), to understand the prediction results and to obtain insight into how tourist satisfaction with local festivals can be improved. Tourist data were collected from local festivals in South Korea over a period of 12 years. We conclude that the proposed predictive and interpretable strategy can identify the strengths and weaknesses of each local festival, allowing festival planners and administrators to enhance their tourist satisfaction rates by addressing the identified weaknesses.
Evaluation and Interpretation of Tourist Satisfaction for Local Korean Festivals Using Explainable AI
In this paper, we propose using explainable artificial intelligence (XAI) techniques to predict and interpret the effects of local festival components on tourist satisfaction. We use data-driven analytics, including prediction, interpretation, and utilization phases, to help festivals establish a tourism strategy. Ultimately, this study aims to identify the most significant variables in local tourism strategy and to predict tourist satisfaction. To do so, we conducted an experimental study to compare the prediction accuracy of representative predictive algorithms. We then built a surrogate model based on a game theory-based framework, known as SHapley Additive exPlanations (SHAP), to understand the prediction results and to obtain insight into how tourist satisfaction with local festivals can be improved. Tourist data were collected from local festivals in South Korea over a period of 12 years. We conclude that the proposed predictive and interpretable strategy can identify the strengths and weaknesses of each local festival, allowing festival planners and administrators to enhance their tourist satisfaction rates by addressing the identified weaknesses.
Evaluation and Interpretation of Tourist Satisfaction for Local Korean Festivals Using Explainable AI
Hoonseong Oh (author) / Sangmin Lee (author)
2021
Article (Journal)
Electronic Resource
Unknown
local festivals , explainable artificial intelligence , Shapley additive explanations , tourism strategy , tourist satisfaction , light gradient boosting machine learning , Environmental effects of industries and plants , TD194-195 , Renewable energy sources , TJ807-830 , Environmental sciences , GE1-350
Metadata by DOAJ is licensed under CC BY-SA 1.0
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